Gene and Cell QC

Set-up

library(here) # for reproducible paths
library(SingleCellExperiment)
library(scater) # For qc
library(org.Mm.eg.db) # To annotate the genenames

sce <- readRDS(here("processed", "sce.RDS"))

Gene QC

We start by removing genes not expressed in any cell

genes_beforeqc <- dim(sce)[1]
keep_feature <- rowSums(counts(sce) > 0) > 0
sce <- sce[keep_feature, ]
genes_beforeqc -dim(sce)[1] 

## [1] 6511

6511 genes are not expressed in any cell.

Add cell QC metrics to the sce

First are sorted the genenames and gene symbols, because the default ensembl notation is not very handy. And then save the mitochondrial genes as such.

if(!file.exists(here("processed", "sce_preliminary.RDS"))){
# obtain full genenames
genename <- mapIds(org.Mm.eg.db, keys = rownames(sce), keytype = "ENSEMBL", column = c("GENENAME"))
# Use the symbols as rownames
# first make gene names unique 
# TODO: save duplicate gene name list
symb_unique <- uniquifyFeatureNames(rownames(sce), rowData(sce)[, "Symbol"])
# Now they can be used as rownames
rownames(sce) <- symb_unique
# Add full gene names and the uniuqe symbols to the rowdata
rowData(sce)$symb_uniq <- symb_unique
rowData(sce)$gene_name <- genename
# Subset the mitochondrial genes
is_mito <- grepl("^mt-", rownames(sce))
}else{
  sce <- readRDS(here("processed", "sce_preliminary.RDS"))
}

Then we can use the scater package to add the quality per cell.

The log transformation for the low thresholds prevents negative thresholds.

if(!file.exists(here("processed", "sce_preliminary.RDS"))){
sce <- addPerCellQC(sce, subsets = list(mt=is_mito)) }
# Automated outlier detection
outlier_lib_low <- isOutlier(sce$total, log = TRUE, type = "lower")
outlier_expr_low <-
  isOutlier(sce$detected, log = TRUE, type = "lower")
outlier_lib_high <- isOutlier(sce$total, type = "higher")
outlier_expr_high <-
  isOutlier(sce$detected, type = "higher")
outlier_mt <- isOutlier(sce$subsets_mt_percent, type = "higher")
# total
outlier <-
  outlier_lib_low |
  outlier_expr_low |
  outlier_lib_high | outlier_expr_high | outlier_mt
# See how many cells are detected as outliers
DataFrame(
  lib_size_high = sum(outlier_lib_high),
  expression_high = sum(outlier_expr_high),
  lib_size_low = sum(outlier_lib_low),
  expression_low = sum(outlier_expr_low),
  mt_pct = sum(outlier_mt),
  total = sum(outlier)
)

## DataFrame with 1 row and 6 columns
##   lib_size_high expression_high lib_size_low expression_low    mt_pct     total
##       <integer>       <integer>    <integer>      <integer> <integer> <integer>
## 1          4059             170            0           2063     18262     24086

# And the thresholds
attr(outlier_lib_high, "thresholds")

##    lower   higher 
##     -Inf 18374.92

attr(outlier_expr_high, "thresholds")

##    lower   higher 
##     -Inf 6876.803

attr(outlier_lib_low, "thresholds")

##    lower   higher 
## 307.5535      Inf

attr(outlier_expr_low, "thresholds")

##    lower   higher 
## 230.9287      Inf

attr(outlier_mt, "thresholds")

##    lower   higher 
##     -Inf 17.54768

# Add if it is an outlier to the metadata
sce$outlier <- outlier

Plots before QC

Diagnostic plots to visualize the data distribution. The orange cells are marked as outliers by the automatic detection from scater. The thresholds are not very useful, so these will poblably be ignored.

Violin plots

plotColData(sce, x="Sample", y="sum", colour_by="outlier") + 
  ggtitle("Total count") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

plotColData(sce, x="Sample", y="sum", colour_by="outlier") + 
  scale_y_log10() + ggtitle("Total count log scale") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

plotColData(sce, x="Sample", y="detected", colour_by="outlier") + 
  scale_y_log10() + ggtitle("Detected Genes") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

plotColData(sce, x="Sample", y="sum", colour_by="chip") + 
  scale_y_log10() + ggtitle("total count by batch") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

plotColData(sce, x="Sample", y="subsets_mt_percent", colour_by="outlier") + ggtitle("Mitocchondrial percentatge") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

Histograms

hist(
    sce$total,
    breaks = 100
)

hist(
    sce$detected,
    breaks = 100
)

hist(
    sce$subsets_mt_percent,
    breaks = 100
)

Scatter plots

plotColData(sce, x="sum", y="subsets_mt_percent", colour_by="outlier")

plotColData(sce, x="sum", y="detected", colour_by="outlier")

plotColData(sce, x="sum", y="detected", colour_by="Sample")

PCA

Here we run a PCA using the information in the metadata instead of the gene expression. It is useful to visualize the QC parametres.

if(!file.exists(here("processed", "sce_preliminary.RDS"))){
sce <- runColDataPCA(sce,  variables = c("sum","detected", "subsets_mt_percent"))
}
plotReducedDim(sce, dimred="PCA_coldata", colour_by = "Sample")

sce$chip <- as.character(sce$chip)
plotReducedDim(sce, dimred="PCA_coldata", colour_by = "chip")

Ratio between sum and gene counts

This measures will probably very useful to get rid of these cells that have low gene counts but a relatively high umi count. The isoutlier function did not work very well with the sum of umi and the detected genes. This function expects roughly a normal distribution. Bellow we try with the ratio between these two parametres, that do follow a normal distribution.

if(!file.exists(here("processed", "sce_preliminary.RDS"))){
sce$ratio_detected_sum <- sce$detected/sce$sum
sce$outlier_ratio <- isOutlier(sce$ratio_detected_sum, type = "both")
}

summary(sce$ratio_detected_sum)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## 0.005707 0.326323 0.395047 0.404313 0.476884 0.878004

plotColData(sce, x="sum", y="detected", colour_by="ratio_detected_sum")

# Plot an histogram with the ratio between umi and gene counts
hist(
    sce$ratio_detected_sum,
    breaks = 100
)

# Use the is outlier function from scater to see the cutoffs suggestions
plotColData(sce, x="sum", y="detected", colour_by="outlier_ratio")

attr(sce$outlier_ratio, "thresholds")

##     lower    higher 
## 0.0649170 0.7251773

The suggestions here are very reasonable, it is not enough to delete all the cells we do not trust but we can definitely take this into consideration as a threshold parameter.

Decide the thresholds

For the genes detected and sum of genes the upper thresholds automatically generated are not very convincing.These thresholds allow to filter doublet cells, but they seem way too harsh on those estimattions. We could start with 50,000 for the number of umi and 7500 for the number of detected genes.

The lower thresholds allow to remove empty droplets and bad quality cells. For these ones we could use the automatic ones, as they seem more reasonable (even though they might be a bit too permissive it is fine for a first filer). These are deleting cells with less than 230.9286948 detected genes and 307.5534791 umi counts.

Finally we will also take in consideration the umi counts/detected genes ratio, that filter out cells with relatively high umi counts but very few detected genes ( lots of copies from the same genes?). As well as the automatic mitochondrial threshold, 17.5476816, this is a bit high compared with other datasets but it is already deleting 18262 cells. Lower down to 10 %, to delete the whole tail would delete 32348 cells.

Here we code the new thersholds

if(!file.exists(here("processed", "sce_preliminary.RDS"))){
# New thresholds
discard_lib_high <- sce$sum > 50000
discard_expr_high <- sce$detected > 7500
discard_expr_low <- outlier_expr_low
discard_lib_low <- outlier_lib_low
discard_ratio <- sce$outlier_ratio
discard_mt <- outlier_mt
sce$discard <- discard_lib_high | discard_expr_high | discard_expr_low | discard_lib_low | discard_ratio | discard_mt 

# See how many cells are detected as outliers
discard <- DataFrame(
  lib_size_high = sum(discard_lib_high),
  expression_high = sum(discard_expr_high),
  lib_size_low = sum(discard_lib_low),
  expression_low = sum(discard_expr_low),
  mt_pct = sum(outlier_mt),
  ratio_umi_genes = sum(discard_ratio),
  total = sum(sce$discard)
)
write.csv(discard, here("outs", "reason_for_discard.csv"))
# Save the object at this stage with the label "preliminary analysis" 
# As only annotation addition has been done, without deleting anything yet.
saveRDS(sce, here("processed", "sce_preliminary.RDS"))}else{
  discard <- read.csv(here("outs", "reason_for_discard.csv"))
}
discard

##   X lib_size_high expression_high lib_size_low expression_low mt_pct
## 1 1            47              69            0           2063  18262
##   ratio_umi_genes total
## 1            2458 21142

Bellow we are going to plot the same plots as before with the decided thresholds